As the volume of electronic personal data stored in databases increases, many companies and organizations today have list filtering systems in place to manage and filter the data.
List filtering systems match a database or record against known content. Conventional list filtering systems do not take into account specific attributes of a known entity to be matched against, and they provide the same filtering results regardless of the end-user or client. However, companies and organizations across different industries and market segments may have different list filtering needs. Depending on numerous variables, the lists checked and the required degree of match may vary for each company or organization. For example, information that may be relevant for a bank in Omaha, Nebr., may not be relevant for another bank in San Diego, Calif. Further, requirements for the San Diego bank may be different from other corporations located in San Diego. Likewise, the list filtering needs for a commercial bank may not be the same as those for a health care insurer/payer. Furthermore, information relating to an account origination application may not be the same as that for an ACH transaction.
As such, there is a need to provide effective list filtering systems that may be customized to each company/organization based on industry, individual entity risk, geographical location, or the like